Overfitting is a modeling error that occurs when a function is too closely fit to a limited set of data points. This happens when a model captures noise and fluctuations in the training data rather than the underlying pattern, leading to poor generalization to new data. Overfitting results in a model that performs well on the training data but poorly on validation or test data. Techniques to prevent overfitting include cross-validation, pruning (in decision trees), regularization, and using simpler models. Identifying and mitigating overfitting is crucial for building robust and reliable predictive models.