Overfitting occurs when a model learns the training data too well — including its noise and quirks — and fails to generalize to new, unseen data. It memorizes rather than learns, acing training examples but failing on real-world inputs. Like a student who memorizes past exams word-for-word but cannot handle rephrased questions. Remedies include regularization, dropout, more training data, data augmentation, and early stopping.