Leave-one-out error refers to a model validation technique where a single observation is removed from the dataset, and the model is trained on the remaining data, with this process repeated for each data point to gain an understanding of the model's predictive accuracy. This approach is a special case of cross-validation, specifically useful for small datasets, but can be computationally expensive for larger datasets.