The sweet spot between underfitting and overfitting is what we term as a good fit. The graph for a model which may generalize well for the given problem is as follows:

A learned function that can perform well enough on unseen data points as well as on the training data is termed a generalizable function. Thus, generalization refers to how well a target function can perform on unseen data, based on the concepts learned during the training phase. The preceding diagram depicts a well generalizing fit.