You use cross$-$validation to set up your model $-$ meaning you divide your data into a training and test data set and then train the model using the training data and use this trained model and make predictions also for your test data. You see that your model fits the training set very well and and also fits the test data quite well $($comparably well than your training set$).$ What is the reason for this outcome and is it good?