The use of local memory is important to improve the performance of OpenCL programs. However, its use may not always benefit performance, depending on various application characteristics, and there is no simple heuristic for deciding when to use it. We develop a machine learning model to decide if the optimization is beneficial or not. We train the model with millions of synthetic benchmarks and show that it can predict if the optimization should be applied for a single array, in both synthetic and real benchmarks, with high accuracy.