The hp.choice(label, options) function in Hyperopt is designed to select one option from a given list or tuple, making it essential for defining categorical hyperparameters during the optimization process. This allows for the exploration of different configurations in machine learning models. The other options describe different Hyperopt functions: hp.randint(label, upper) for random integers, hp.uniform(label, low, high) for uniform distribution between two values, and hp.normal(label, mu, sigma) for values following a normal distribution.