Which technique is often used for improving the robustness of machine learning models?

Utilizing cross-validation techniques is a widely recognized approach for enhancing the robustness of machine learning models. Cross-validation involves splitting the training dataset into multiple subsets, or "folds," and training the model on a portion of the data while validating it on the remaining part. This process is repeated several times, allowing the model to be trained and evaluated on different subsets of the data.

The primary advantage of cross-validation is that it provides a more comprehensive assessment of the model’s performance across various segments of the dataset, reducing the likelihood of overfitting to a specific training set. By averaging the performance metrics over multiple iterations, the model's robustness is better evaluated, leading to more reliable predictions when encountering new, unseen data.

In contrast, training on a single dataset may lead to a model that performs well on that particular set but fails to generalize effectively. Relying solely on data preprocessing can certainly improve data quality but does not inherently enhance model robustness. While reducing model complexity can help in certain contexts, it is not a guaranteed method for improving robustness across all scenarios.