The document discusses overfitting in machine learning models and how regularization helps address this problem. It explains that overfitting occurs when a model fits the training data too closely and fails to generalize to new data, resulting in large errors. Regularization helps reduce overfitting by penalizing model complexity, allowing the model to fit the training data while maintaining the ability to generalize to new observations. The document provides examples of different regularization techniques including ridge regression, lasso, and elastic net.