Predictive model validation is the process of evaluating how well a trained machine learning model generalizes to independent, unseen data. In practice, this involves holding out a portion of the data that was not used for training (e.g. a test set or using techniques like cross-validation) and then assessing the model’s performance on this data​.The idea is to obtain an unbiased estimate of the model’s predictive accuracy or error on new inputs. Common validation techniques include: hold-out validation, where the dataset is split into a training set and test set; k-fold cross-validation, where the data is repeatedly split and the model is trained and tested k times to average out variability; and leave-one-out validation, a special case of cross-validation​.During validation, one might compute metrics such as accuracy, F1-score, RMSE, etc., or use procedures like statistical significance tests to compare models. Effective predictive model validation helps guard against overfitting – if a model performs well on training data but poorly on validation data, it’s likely too complex and has memorized noise. By contrast, a model that also performs strongly on validation sets is considered to generalize well. In summary, predictive model validation is a crucial step in the modeling pipeline to ensure that the predictive insights or decisions a model provides will hold up on new, real-world data​.