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 multiple subsets, where each subset contains all but one data point. The model is trained on the remaining data and tested on the excluded point, repeating this process for each data point in the dataset. This approach allows for a thorough evaluation since every data point is used for both training and testing. However, LOOCV can be computationally expensive, especially with large datasets, as it requires training the model multiple times, once for each data point.