As we all know from machine learning 101, you should split your dataset into three parts: the training, validation, and test set. You train your models on the training set. You choose your hyperparameters by selecting the best model from the validation set. Finally, you look at your accuracy (F1 score, ROC curve...) on the test set. And voilà, you’ve just achieved XYZ% accuracy.