What does model validation ensure in machine learning?

Model validation is a crucial process in machine learning that aims to assess how well a model generalizes to new, unseen data. It involves using techniques such as cross-validation, where the dataset is split into training and testing subsets. The model is trained on one subset and validated on another to evaluate its performance and adjust parameters as needed.

The primary goal of model validation is to ensure that the model is not merely memorizing the training data—often referred to as overfitting—but rather learning patterns that can be generalized to make accurate predictions on data it has not encountered before. This is essential for creating robust models that perform effectively in real-world applications.

To summarize, effective model validation is focused on the model's ability to predict outcomes accurately on unseen datasets, which is reflected in the understanding that models should perform well on unseen data.