CrossValidationReporter - add a roc function

[MarieS-WiMLDS]

Is your feature request related to a problem? Please describe.

When I do a cross validation, for a classification problem, I'd like to know and see how it goes in terms of roc curves.

Describe the solution you'd like

cv_report = skore.CrossValidationReporter(est, X, y)
cv_report.plots.roc

example in scikit-learn doc with matplotlib, you might want to rewrite it with plotly.

import matplotlib.pyplot as plt

from sklearn import svm
from sklearn.metrics import RocCurveDisplay, auc
from sklearn.model_selection import StratifiedKFold

n_splits = 6
cv = StratifiedKFold(n_splits=n_splits)
classifier = svm.SVC(kernel="linear", probability=True, random_state=random_state)

tprs = []
aucs = []
mean_fpr = np.linspace(0, 1, 100)

fig, ax = plt.subplots(figsize=(6, 6))
for fold, (train, test) in enumerate(cv.split(X, y)):
    classifier.fit(X[train], y[train])
    viz = RocCurveDisplay.from_estimator(
        classifier,
        X[test],
        y[test],
        name=f"ROC fold {fold}",
        alpha=0.3,
        lw=1,
        ax=ax,
        plot_chance_level=(fold == n_splits - 1),
    )
    interp_tpr = np.interp(mean_fpr, viz.fpr, viz.tpr)
    interp_tpr[0] = 0.0
    tprs.append(interp_tpr)
    aucs.append(viz.roc_auc)

ax.set(
    xlabel="False Positive Rate",
    ylabel="True Positive Rate",
    title=f"Mean ROC curve with variability\n(Positive label '{target_names[1]}')",
)
ax.legend(loc="lower right")
plt.show()

Describe alternatives you've considered, if relevant

ploting the "median" roc and the +/- 1 STD. but it's weird and unclear if it has scientific value (cc @glemaitre for confirmation).

Additional context

No response