Which of the following techniques is used to ensure a model is robust and generalizes well to unseen data?

Cross-validation is a crucial technique used in model evaluation to ensure that a model is robust and can generalize well to unseen data. By partitioning the dataset into multiple subsets, cross-validation allows the model to be trained on different subsets while being validated on others. This process helps in assessing the model's performance under various conditions and reduces the risk of overfitting, where a model performs well on the training data but poorly on new, unseen data.

The primary goal of cross-validation is to provide an understanding of how the model is expected to perform in practice, thus enhancing its reliability for real-world applications. Various forms of cross-validation, such as k-fold or leave-one-out, can be employed depending on the dataset size and complexity.

Other techniques listed, such as batch processing, data smoothing, and data aggregation, serve different purposes in data management and preparation rather than directly addressing model robustness or generalization features. Batch processing is about how data is handled in groups, data smoothing pertains to reducing noise in data, and data aggregation involves summarizing data. None of these techniques focus specifically on evaluating a model's ability to generalize to new data, which is why cross-validation stands out as the most relevant method for ensuring a model's robustness.