Overfitting happens when a model captures noise in the training data, leading it to perform exceptionally well on that data but generalize poorly to unseen data. This can occur when a model is too complex relative to the amount of training data available, such as a deep neural network trained on a small dataset. Techniques like cross-validation and regularization help mitigate overfitting.