Tikhonov regularization is a mathematical technique used to solve ill-posed problems, particularly in the field of machine learning and statistics. It adds a penalty term to the loss function, which helps to stabilize the solution by discouraging overly complex models. This is especially useful when the data is noisy or when there are more parameters than observations. The method works by incorporating a regularization parameter that controls the trade-off between fitting the data and maintaining simplicity in the model. By adjusting this parameter, one can achieve a balance that improves the model's generalization to new data, thus enhancing its predictive performance.