Regularization is a set of techniques that prevent a model from memorizing its training data instead of learning general patterns, a failure called overfitting. Common methods penalize overly large weights (L1, L2), randomly disable neurons during training (dropout), or stop training early. Think of a student encouraged to understand concepts rather than memorize past exams: they perform better on new questions.