Model Training is the process of teaching a machine learning model to make predictions or decisions based on data. This involves feeding the model a large set of examples, known as the training dataset, which includes input data and the corresponding correct outputs. The model learns patterns and relationships within the data to improve its accuracy. Once the model has been trained, it can be evaluated using a separate set of data called the validation dataset. This helps to ensure that the model can generalize its learning to new, unseen data, rather than just memorizing the training examples.