Multi-node clusters in Databricks are designed to distribute computations across several nodes, enabling parallel processing and significantly boosting computational power. This setup is ideal for processing large datasets and executing complex machine learning algorithms that benefit from distributed computing. In contrast, single-node clusters are better suited for lighter workloads, whereas multi-node clusters are optimized for scaling and meeting higher computational demands.