I have a deep learning configuration in which I obtain good results on the validation set but even better results in the training set. From my understanding this means that there is overfitting to some extent. What does this mean in practice? Does it mean that my model is not good and that I should not use it? If I decrease the gap between the validation and training accuracy (decreasing the overfitting) but at the same time decrease the validation accuracy, which of the two models is better?
Below are some images to illustrate the two situations outlined previously: