Overfitting occurs when a machine learning model learns the training data too well, capturing noise and outliers instead of the underlying patterns. This means the model performs excellently on the training data but struggles with new, unseen data. Imagine a student who memorizes answers for a test but fails to understand the subject; they may ace that specific test but won't do well in real-life applications. To prevent overfitting, techniques like cross-validation, regularization, and pruning are used. These methods help ensure that the model generalizes better, allowing it to make accurate predictions on new data. Think of it as teaching a student to understand concepts rather than just memorizing facts.