"Train models" refers to the process of teaching a machine learning algorithm to recognize patterns in data. This involves feeding the model a large set of examples, known as the training dataset, which helps it learn how to make predictions or decisions based on new, unseen data. During training, the model adjusts its internal parameters to minimize errors in its predictions. Once trained, the model can be evaluated using a separate dataset, called the validation dataset, to ensure it performs well and generalizes effectively to new situations.