The text discusses the hold-out method, a technique used in the training, evaluation, and selection of machine learning models to address the issue of overfitting, which occurs when a model performs well on training data but poorly on unseen data. This method involves splitting a dataset into separate training and testing sets, often using a typical 70-30% split, to ensure that the model can generalize to new data. The hold-out method helps in selecting the best model by evaluating its performance on the test dataset, with the goal of minimizing generalization error. Additionally, it can be used in conjunction with hyperparameter tuning and adjusting model selection processes. The text also references a practical example using Python's Sklearn library to demonstrate how to implement this technique, highlighting its utility in preventing overfitting and underfitting, reducing error pruning in decision trees, and supporting early stopping in neural networks.