The skip gram method is a key component of Google's Word2Vec, a popular tool for learning word embeddings. Word2Vec uses the skip gram model to learn high-quality word embeddings that capture the semantic relationships between words. This allows for more accurate and efficient text processing in various natural language processing tasks, such as sentiment analysis, machine translation, and named entity recognition.