Regularization is a technique used in machine learning to prevent a model from becoming too complex and overfitting the training data. Overfitting occurs when a model learns the noise in the data rather than the underlying patterns, leading to poor performance on new, unseen data. By adding a penalty for complexity, regularization encourages the model to focus on the most important features, resulting in better generalization. There are different types of regularization methods, such as L1 regularization and L2 regularization. L1 regularization can lead to sparse models by driving some feature weights to zero, while L2 regularization tends to distribute weights more evenly. Both methods help improve the model's performance and robustness.