The Skip-Gram Model is a neural network-based approach used in natural language processing to predict context words given a target word, effectively capturing semantic relationships between words. It is particularly effective for learning high-quality word embeddings by maximizing the probability of context words appearing within a fixed window around a target word in a corpus.