diff --git a/examples/getting_started/plot_quick_start.py b/examples/getting_started/plot_quick_start.py
index 421ca0328..040a738e8 100644
--- a/examples/getting_started/plot_quick_start.py
+++ b/examples/getting_started/plot_quick_start.py
@@ -42,9 +42,6 @@
 # %%
 reporter.plots.scores
 
-# %%
-reporter.plots.timing_normalized
-
 # %%
 # Finally, from your shell (in the same directory), start the UI:
 #
diff --git a/skore/src/skore/item/cross_validation_item.py b/skore/src/skore/item/cross_validation_item.py
index e4873f3a0..205949744 100644
--- a/skore/src/skore/item/cross_validation_item.py
+++ b/skore/src/skore/item/cross_validation_item.py
@@ -344,7 +344,6 @@ def factory(cls, reporter: CrossValidationReporter) -> CrossValidationItem:
         humanized_plot_names = {
             "scores": "Scores",
             "timing": "Timings",
-            "timing_normalized": "Normalized timings",
         }
         plots_bytes = {
             humanized_plot_names[plot_name]: (
diff --git a/skore/src/skore/sklearn/cross_validation/cross_validation_reporter.py b/skore/src/skore/sklearn/cross_validation/cross_validation_reporter.py
index 8dc52ab16..47d9b7d09 100644
--- a/skore/src/skore/sklearn/cross_validation/cross_validation_reporter.py
+++ b/skore/src/skore/sklearn/cross_validation/cross_validation_reporter.py
@@ -16,7 +16,6 @@
     _strip_cv_results_scores,
 )
 from .plots.compare_scores_plot import plot_cross_validation_compare_scores
-from .plots.timing_normalized_plot import plot_cross_validation_timing_normalized
 from .plots.timing_plot import plot_cross_validation_timing
 
 
@@ -26,7 +25,6 @@ class CrossValidationPlots:
 
     scores: plotly.graph_objects.Figure
     timing: plotly.graph_objects.Figure
-    timing_normalized: plotly.graph_objects.Figure
 
 
 class CrossValidationReporter:
@@ -189,5 +187,4 @@ def plots(self) -> CrossValidationPlots:
         return CrossValidationPlots(
             scores=plot_cross_validation_compare_scores(self._cv_results),
             timing=plot_cross_validation_timing(self._cv_results),
-            timing_normalized=plot_cross_validation_timing_normalized(self._cv_results),
         )
diff --git a/skore/src/skore/sklearn/cross_validation/plots/timing_normalized_plot.py b/skore/src/skore/sklearn/cross_validation/plots/timing_normalized_plot.py
deleted file mode 100644
index 767fee460..000000000
--- a/skore/src/skore/sklearn/cross_validation/plots/timing_normalized_plot.py
+++ /dev/null
@@ -1,133 +0,0 @@
-"""Plot cross-validation normalized timing results."""
-
-from __future__ import annotations
-
-from typing import TYPE_CHECKING
-
-from numpy import linspace
-
-if TYPE_CHECKING:
-    import plotly.graph_objects
-
-
-def plot_cross_validation_timing_normalized(
-    cv_results: dict,
-) -> plotly.graph_objects.Figure:
-    """Plot the normalized timing results of a cross-validation run.
-
-    Each timing result is normalized by the number of data points.
-
-    Parameters
-    ----------
-    cv_results : dict
-        The output of scikit-learn's cross_validate function.
-
-    Returns
-    -------
-    plotly.graph_objects.Figure
-        A plot of the normalized time-related cross-validation results.
-    """
-    from datetime import timedelta
-
-    import pandas
-    import plotly
-    import plotly.graph_objects as go
-
-    _cv_results = cv_results.copy()
-
-    # Remove irrelevant keys
-    to_remove = [
-        key
-        for key in _cv_results
-        if key not in ["fit_time_per_data_point", "score_time_per_data_point"]
-    ]
-    for key in to_remove:
-        _cv_results.pop(key, None)
-
-    df = pandas.DataFrame(_cv_results)
-
-    dict_labels = {
-        "fit_time_per_data_point": "fit_time_per_data_point (seconds)",
-        "score_time_per_data_point": "score_time_per_data_point (seconds)",
-    }
-
-    fig = go.Figure()
-
-    for col_i, col_name in enumerate(df.columns):
-        metric_name = dict_labels.get(col_name, col_name)
-        bar_color = plotly.colors.qualitative.Plotly[
-            col_i % len(plotly.colors.qualitative.Plotly)
-        ]
-        bar_x = linspace(min(df.index) - 0.5, max(df.index) + 0.5, num=10)
-
-        common_kwargs = dict(
-            visible=True if col_i == 0 else "legendonly",
-            legendgroup=f"group{col_i}",
-        )
-
-        # Calculate statistics
-        avg_value = df[col_name].mean()
-        std_value = df[col_name].std()
-
-        # Add all traces at once
-        fig.add_traces(
-            [
-                # Bar trace
-                go.Bar(
-                    x=df.index,
-                    y=df[col_name].values,
-                    name=metric_name,
-                    marker_color=bar_color,
-                    showlegend=True,
-                    hovertemplate=(
-                        "%{customdata}" f"<extra>{col_name} (timedelta)</extra>"
-                    ),
-                    customdata=[str(timedelta(seconds=x)) for x in df[col_name].values],
-                    **common_kwargs,
-                ),
-                # Mean line
-                go.Scatter(
-                    x=bar_x,
-                    y=[avg_value] * 10,
-                    name=f"Average {metric_name}",
-                    line=dict(dash="dash", color=bar_color),
-                    showlegend=False,
-                    mode="lines",
-                    hovertemplate="%{customdata}",
-                    customdata=[str(timedelta(seconds=avg_value))] * 10,
-                    **common_kwargs,
-                ),
-                # +1 std line
-                go.Scatter(
-                    x=bar_x,
-                    y=[avg_value + std_value] * 10,
-                    name=f"Average + 1 std. dev. {metric_name}",
-                    line=dict(dash="dot", color=bar_color),
-                    showlegend=False,
-                    mode="lines",
-                    hovertemplate="%{customdata}",
-                    customdata=[str(timedelta(seconds=avg_value + std_value))] * 10,
-                    **common_kwargs,
-                ),
-                # -1 std line
-                go.Scatter(
-                    x=bar_x,
-                    y=[avg_value - std_value] * 10,
-                    name=f"Average - 1 std. dev. {metric_name}",
-                    line=dict(dash="dot", color=bar_color),
-                    showlegend=False,
-                    mode="lines",
-                    hovertemplate="%{customdata}",
-                    customdata=[str(timedelta(seconds=avg_value + std_value))] * 10,
-                    **common_kwargs,
-                ),
-            ]
-        )
-
-    fig.update_xaxes(tickmode="linear", dtick=1, title_text="Split index")
-    fig.update_yaxes(title_text="Value")
-    fig.update_layout(
-        title_text="Normalized time-related cross-validation results for each split"
-    )
-
-    return fig
diff --git a/skore/tests/unit/item/test_cross_validation_item.py b/skore/tests/unit/item/test_cross_validation_item.py
index bd9da08d6..3c3e557bf 100644
--- a/skore/tests/unit/item/test_cross_validation_item.py
+++ b/skore/tests/unit/item/test_cross_validation_item.py
@@ -37,7 +37,6 @@ class FakeCrossValidationReporter(CrossValidationReporter):
     plots = CrossValidationPlots(
         scores=plotly.graph_objects.Figure(),
         timing=plotly.graph_objects.Figure(),
-        timing_normalized=plotly.graph_objects.Figure(),
     )
     cv = StratifiedKFold(n_splits=5)
 
@@ -59,7 +58,6 @@ class FakeCrossValidationReporterNoGetParams(CrossValidationReporter):
     plots = CrossValidationPlots(
         scores=plotly.graph_objects.Figure(),
         timing=plotly.graph_objects.Figure(),
-        timing_normalized=plotly.graph_objects.Figure(),
     )
     cv = StratifiedKFold(n_splits=5)