What does leave-one-out validation involve?

Leave-one-out validation is a specific type of cross-validation used in machine learning and statistical analysis. This approach involves taking one individual data point from the dataset to be used as the validation or test set, while the remaining data points are utilized for training the model. This method is particularly useful in scenarios where the dataset is small, as it allows the model to be trained on nearly all available data while still providing an independent evaluation of its performance.

By iterating this process across the entire dataset, where each data point serves as the test set once, the leave-one-out approach produces a comprehensive understanding of how the model might perform on unseen data. This thorough evaluation helps in assessing the model's robustness and can highlight potential overfitting or underfitting issues.

The other options do not accurately reflect the leave-one-out approach, as they describe different validation methods or strategies that do not involve using a single observation for testing in conjunction with the rest for training.