What is a key advantage of using shrinkage methods in statistics?

Shrinkage methods, such as Ridge and Lasso regression, are designed to reduce the complexity of statistical models. A key advantage of these methods is their ability to prevent overfitting, which occurs when a model is too closely fitted to training data, resulting in poor performance on unseen data.

By adding a penalty to the loss function during model training, shrinkage methods effectively constrain the coefficient estimates. This discourages overly complex models that might capture noise rather than the underlying data patterns. Consequently, these techniques enhance the model's generalizability and robustness, especially in situations where the number of predictors is large relative to the sample size or when predictors are highly correlated.

The other options highlight aspects that do not align with the core benefits of shrinkage methods: they may not directly impact computation time, could potentially decrease interpretability if the model becomes more complex, and are unlikely to increase multicollinearity, as they often help to mitigate issues related to multicollinearity by stabilizing coefficient estimates.