Hyperparameters are settings or configurations used to control the training process of machine learning models. Unlike model parameters, which are learned from the data, hyperparameters are set before training begins. Examples include the learning rate, batch size, and the number of epochs. Choosing the right hyperparameters can significantly affect the model's performance. Tuning hyperparameters often involves experimentation and can be done using techniques like grid search or random search. Tools like scikit-learn and TensorFlow provide functionalities to help automate this process. Proper hyperparameter tuning can lead to better accuracy and efficiency in model training.