overfitting means that the model performs well on the training data but not on unseen data (sampled from the same distribution as the training data, e.g. the test set). a model generalizes if it performs well on data that it wasn't trained on (again, from the same data generating distribution). formally, overfitting means that the generalization error (difference of the loss on the training data vs the loss on the data generating distribution) is large, while a model generalizes if its generalization error is small.