docs: def fit_select_best(X, y):
      """
      Selects the best fit of the estimators already implemented by choosing the
      model with the smallest mean square error metric for the trained values.
      """
      models = [fit(X,y) for fit in [fit_linear, fit_quadratic]]
      errors = map(lambda model: mse(y, model.predict(X)), models)
  
      return min(zip(models, errors), key=itemgetter(1))[0]