The regularization parameter is a hyperparameter in machine learning models that controls the trade-off between fitting the training data and maintaining model simplicity to prevent overfitting. By adjusting this parameter, one can fine-tune the model's ability to generalize to unseen data, balancing bias and variance effectively.