Regularization techniques are methods used in machine learning to prevent overfitting, which occurs when a model learns the noise in the training data instead of the underlying patterns. By adding a penalty for complexity, these techniques help ensure that the model generalizes well to new, unseen data. Common regularization methods include L1 regularization and L2 regularization, which add different types of penalties to the loss function. Another popular regularization technique is dropout, often used in neural networks. During training, dropout randomly ignores a portion of the neurons, forcing the model to learn more robust features. This helps improve the model's performance on test data, making it more reliable in real-world applications.