Fits the learning curve with the wrapped model to the specified data.
        Draws training and test score curves and saves the scores to the
        estimator.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape (n_samples) or (n_samples, n_features), optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

        Returns
        -------
        self : instance

def fit(self, X, y, **kwargs):
        """
        Fits the learning curve with the wrapped model to the specified data.
        Draws training and test score curves and saves the scores to the
        estimator.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            Training vector, where n_samples is the number of samples and
            n_features is the number of features.

        y : array-like, shape (n_samples) or (n_samples, n_features), optional
            Target relative to X for classification or regression;
            None for unsupervised learning.

        Returns
        -------
        self : instance

        """

        self.cv_scores_ = cross_val_score(
            self.estimator, X, y, cv=self.cv, scoring=self.scoring
        )
        self.cv_scores_mean_ = self.cv_scores_.mean()

        self.draw()
        return self