Hyperparameters are the parameters in a machine learning model that are set before the training process begins. Unlike model parameters that are learned from the training data, hyperparameters control aspects such as the learning rate, batch size, and number of epochs. Tuning hyperparameters is critical for optimizing model performance and achieving better results.