A Gaussian Process is a collection of random variables, any finite number of which have a joint Gaussian distribution, used in machine learning to define a distribution over functions. It is particularly useful for regression tasks due to its ability to provide a probabilistic prediction with uncertainty quantification, making it a powerful tool for modeling complex data with inherent noise.