Hyperparameter space refers to the multidimensional space that represents all possible combinations of hyperparameters for a machine learning model, which can be explored to optimize model performance. Efficient exploration of this space is crucial for achieving the best model configuration, often involving techniques like grid search, random search, or more advanced methods like Bayesian optimization.