What is the primary goal of selecting a model in the validation phase?

The primary goal of selecting a model in the validation phase is to improve generalization. Generalization refers to a model's ability to perform well on new, unseen data, which is a critical aspect of the modeling process. During the validation phase, multiple models are typically evaluated on their performance using a separate validation dataset that was not involved in the training process. This helps to assess how well each model will translate its learned patterns to new data.

When striving for improved generalization, practitioners focus on finding a model that balances bias and variance, ensuring that it is neither too overly simplified (leading to high bias and poor performance on training and validation data) nor too complex (resulting in overfitting, where the model performs well on the training data but poorly on new data). Selecting a model that generalizes well involves utilizing validation metrics to select the model that demonstrates the best performance on the validation set, thereby optimizing its applicability to future data.

The other options do not align with the main goal of the validation phase. While maximizing training data, minimizing model complexity, and increasing accuracy are all relevant considerations in the modeling process, they do not directly articulate the overarching purpose of ensuring that the model can generalize effectively to new data.