What is the primary advantage of using k-fold cross-validation over a single train-test split in model evaluation?

What is the primary purpose of a validation dataset in machine learning?

A. To train the model

B. To evaluate the model on unseen data

C. To test the model's performance on training data

D. To visualize data relationships

Which metric is commonly used to evaluate classification models and represents the ratio of correctly predicted positive instances to all positive instances?

A. Accuracy

B. Precision

C. Recall

D. F1 Score

In k-fold cross-validation, if you choose a higher value of k (e.g., k = 10), what effect does it have on the model evaluation process?

A. It reduces the risk of overfitting

B. It reduces the number of folds used in training

C. It increases the model's complexity

D. It decreases the training time

Which technique is used to prevent data leakage in cross-validation, ensuring that information from the test set doesn't influence model training?

A. Leave-One-Out Cross-Validation (LOOCV)

B. Stratified Sampling

C. Holdout Validation

D. Feature Scaling