Problem transformation is a strategy in machine learning where a complex problem is converted into a simpler, often more familiar, problem that can be solved using existing techniques. This approach is particularly useful in multi-label classification, where it transforms the task into multiple binary classification problems or other formats that are easier to handle computationally.