Hoeffding trees are designed to handle large-scale datasets by processing data streams incrementally. They use the Hoeffding Bound to make decisions on when to split nodes, allowing for real-time learning without the need to store large amounts of data for future reprocessing. This makes them particularly suitable for online learning in large-scale datasets, where traditional decision tree induction algorithms may struggle due to storage requirements.