Correct Answer: C. When computational resources and time are constrained

Explanation: A single Train-Test Split is computationally more efficient than Cross-Validation because it involves splitting the dataset only once into training and testing sets. Cross-Validation, on the other hand, requires multiple splits and training sessions, which can be resource-intensive and time-consuming, especially with large datasets or complex models. Therefore, in scenarios where computational efficiency is a priority, a single Train-Test Split is preferable.

Why Not the Others?

• A. Ensuring model stability and generalization: Cross-Validation is generally better for this purpose as it provides a more robust estimate of the model's performance across different subsets of the data.
• B. Imbalanced datasets: Neither method inherently addresses imbalance. Techniques like stratified sampling or resampling are more relevant here.
• D. Maximizing model performance: Cross-Validation is preferred for achieving higher model performance and reliability by evaluating the model on multiple unseen data subsets.