What is the primary purpose of regularization in statistical models?

Regularization is a technique used in statistical models primarily to address the issue of overfitting. Overfitting occurs when a model becomes too complex and captures noise in the training data rather than the underlying patterns, leading to poor generalization to new, unseen data.

By introducing a regularization term into the model's loss function, regularization discourages excessively complex models by penalizing large coefficients. This results in more robust models that perform better on previously unseen data by striking a balance between fitting the training data well and maintaining simplicity. Consequently, regularization helps in achieving better generalization and reducing the chances of overfitting, which is why this option is the correct answer.

Other choices do not align with the primary purpose of regularization. Increasing model complexity contradicts the goal of regularization, which is to create simpler models. Simplifying the data is not the direct function of regularization, as it focuses more on the model's structure than the data itself. While regularization may have some impact on computational efficiency by potentially simplifying models, minimizing computational cost is not its primary intent.