Model trimming is a technique used to reduce the size and complexity of machine learning models by removing unimportant parameters or layers, thus enhancing efficiency without significantly affecting performance. This approach is crucial for deploying models in resource-constrained environments, such as mobile devices, while maintaining accuracy and speed.