Leave-One-Out Cross-Validation (LOOCV) is a technique used to assess the performance of a machine learning model. In this method, the dataset is divided into training and testing sets by leaving out one data point at a time. The model is trained on all the remaining data points and then tested on the single left-out point. This process is repeated for each data point in the dataset. LOOCV provides a thorough evaluation since every data point is used for testing exactly once. However, it can be computationally expensive, especially with large datasets, as it requires training the model multiple times, once for each data point.