Tensor Train Decomposition is a method for efficiently representing high-dimensional tensors by decomposing them into a sequence of lower-dimensional matrices, significantly reducing the complexity and storage requirements. This technique is particularly useful in fields like machine learning and scientific computing where handling large-scale data is essential.