keras plotting loss and MSE
cross_val_score does not return the history of the training. You can use fit instead: 

history = model.fit( ...


See this example.

As you mentioned, the history object holds the results of the training for each epoch.

Here is the relevant bit:

history = model.fit(X, X, epochs=500, batch_size=len(X), verbose=2)
pyplot.plot(history.history['mean_squared_error'])
pyplot.plot(history.history['mean_absolute_error'])
pyplot.plot(history.history['mean_absolute_percentage_error'])
pyplot.plot(history.history['cosine_proximity'])
pyplot.show()


I was actually working on the same example that you referenced yesterday. I think it is hard to understand because it introduces many functions and concepts: estimators, StandardScaler, KerasRegressor, Pipeline, KFold and cross_val_score.

However, I did like the approach to creating and testing models, and Cross Validation would produce more robust models.