Hyperparameters are specific settings in a model configuration that need to be set prior to the learning process. Unlike parameters, which are learned during training, hyperparameters are predefined and dictate the model's behavior, such as the learning rate or the number of layers in a neural network. These choices can significantly affect the model's performance, making hyperparameter tuning crucial for achieving optimal results.