The "Bag of Words" model is a simple way to represent text data in natural language processing. It treats a document as a collection of words, disregarding grammar and word order. Each unique word in the document is counted, creating a "bag" that reflects the frequency of each word's occurrence. This model is commonly used in tasks like text classification and sentiment analysis. By converting text into numerical data, algorithms can easily analyze and compare documents. However, it has limitations, such as losing context and meaning, which can affect the accuracy of more complex analyses.