In the context of artificial intelligence and machine learning, a model refers to a computational framework that simulates a process or system based on data. Models are trained using historical data, allowing them to recognize patterns and relationships within that data. Once trained, they can make predictions or decisions about new, unseen data. The process involves selecting an appropriate algorithm, training the model on a dataset, and evaluating its performance using various metrics. Models can be linear or complex, depending on the problem being solved, and their effectiveness is often enhanced through techniques such as feature engineering, hyperparameter tuning, and regularization.