docs: 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