In the development of a machine learning model for predicting customer churn, you are tasked with evaluating whether a new feature significantly improves the model's performance compared to the baseline model without the feature. The dataset is large, and the team is concerned about both Type I and Type II errors. Which of the following approaches is the MOST appropriate for this scenario, and why? Choose the best option.