Skip-Gram is a model used in natural language processing to learn word embeddings. It predicts surrounding words in a sentence given a target word. For example, if the target word is "dog," the model tries to predict words like "barks" or "runs" that commonly appear near it. This approach helps capture the context and meaning of words based on their usage in large text datasets. By training on many sentences, Skip-Gram creates a vector representation for each word, allowing for better understanding and analysis of language relationships in tasks like machine learning and text analysis.