Which of the following best describes 'regularization'?

Regularization is a technique specifically designed to simplify models to avoid overfitting, which occurs when a model becomes too complex and learns the noise in the training data rather than the underlying pattern. By adding a penalty for complexity to the loss function during training, regularization methods, such as L1 (Lasso) or L2 (Ridge) regularization, constrain the model parameters, effectively reducing their values and promoting simpler models that generalize better on unseen data.

This approach is essential because simpler models tend to have better predictive performance on new, unseen data, as they maintain the essential patterns without capturing the noise. Therefore, focusing on this aspect of regularization highlights its crucial role in balancing bias and variance in model training.

The other options do not accurately represent what regularization does. Increasing model complexity is contrary to the goal of regularization, which seeks simplicity. Improving model training speed is unrelated to regularization; instead, it focuses on performance optimization. Lastly, while regularization can indirectly enhance model accuracy by preventing overfitting, its primary function is to reduce complexity rather than to directly improve accuracy.