The fit method is the primary entry point for the manual alpha
        selection visualizer. It sets the alpha param for each alpha in the
        alphas list on the wrapped estimator, then scores the model using the
        passed in X and y data set. Those scores are then aggregated and
        drawn using matplotlib.

def fit(self, X, y, **args):
        """
        The fit method is the primary entry point for the manual alpha
        selection visualizer. It sets the alpha param for each alpha in the
        alphas list on the wrapped estimator, then scores the model using the
        passed in X and y data set. Those scores are then aggregated and
        drawn using matplotlib.
        """
        self.errors = []
        for alpha in self.alphas:
            self.estimator.set_params(alpha=alpha)
            scores = self.score_method(self.estimator, X, y)
            self.errors.append(scores.mean())

        # Convert errors to an ND array and draw
        self.errors = np.array(self.errors)
        self.draw()

        # Always make sure to return self from fit
        return self