After a bit of resaerch I found out that I can use pipeline in order to avoid data leakage.

references: this and this

This is how my code looks like now:

    def produce_learning_curve(self, model, model_name, nb_splits, output_folder, parameters=None, nb_repeats=None):
 

        X_train, y_train = self.X_train, self.y_train

        pipe = Pipeline([
            ('sc', StandardScaler()),
            ('model', model(**parameters, random_state=42))
        ])

        print('Inside Learning curve')
        print('X_train: ', self.X_train.shape)
        print('y_train: ', self.y_train.shape)

        if nb_repeats is None:
            cv = StratifiedKFold(n_splits=nb_splits, random_state=42)
        else:
            cv = RepeatedStratifiedKFold(n_splits=nb_splits, n_repeats=nb_repeats, random_state=42)

        # train_sizes, train_scores, test_scores = learning_curve(model(**parameters), X_train, y_train, cv=cv, scoring='accuracy')  # calculate learning curve values

        train_sizes, train_scores, test_scores = learning_curve(pipe, X_train, y_train, cv=cv, scoring='accuracy')  # calculate learning curve values

        # mean of the results of the training and testing
        train_scores_mean = np.mean(train_scores, axis=1)
        test_scores_mean = np.mean(test_scores, axis=1)

        plt.figure()
        plt.xlabel("Number of Training Samples")
        plt.ylabel("Accuracy")

        plt.plot(train_sizes, train_scores_mean, label="training")
        plt.plot(train_sizes, test_scores_mean, label="validation")
        plt.legend()

        if not os.path.exists(output_folder):
            os.makedirs(output_folder)
        plt.savefig(output_folder + '%s_learning_curve.png' % model_name)
        plt.close()
```

After a bit of research I found out that I can use pipeline in order to avoid data leakage.

references: this and this

This is how my code looks like now:

    def produce_learning_curve(self, model, model_name, nb_splits, output_folder, parameters=None, nb_repeats=None):

        X_train, y_train = self.X_train, self.y_train

        pipe = Pipeline([
            ('sc', StandardScaler()),
            ('model', model(**parameters, random_state=42))
        ])

        print('Inside Learning curve')
        print('X_train: ', self.X_train.shape)
        print('y_train: ', self.y_train.shape)

        if nb_repeats is None:
            cv = StratifiedKFold(n_splits=nb_splits, random_state=42)
        else:
            cv = RepeatedStratifiedKFold(n_splits=nb_splits, n_repeats=nb_repeats, random_state=42)

        # train_sizes, train_scores, test_scores = learning_curve(model(**parameters), X_train, y_train, cv=cv, scoring='accuracy')  # calculate learning curve values

        train_sizes, train_scores, test_scores = learning_curve(pipe, X_train, y_train, cv=cv, scoring='accuracy')  # calculate learning curve values

        # mean of the results of the training and testing
        train_scores_mean = np.mean(train_scores, axis=1)
        test_scores_mean = np.mean(test_scores, axis=1)

        plt.figure()
        plt.xlabel("Number of Training Samples")
        plt.ylabel("Accuracy")

        plt.plot(train_sizes, train_scores_mean, label="training")
        plt.plot(train_sizes, test_scores_mean, label="validation")
        plt.legend()

        if not os.path.exists(output_folder):
            os.makedirs(output_folder)
        plt.savefig(output_folder + '%s_learning_curve.png' % model_name)
        plt.close()
```