A Transformer Block is a fundamental building unit of the Transformer architecture, which uses self-attention mechanisms to process input data in parallel, making it highly effective for natural language processing tasks. It consists of multi-head attention, feed-forward neural networks, and layer normalization, enabling efficient handling of long-range dependencies in sequences.