Cross validation is a statistical technique used to evaluate the performance of a model by partitioning the original dataset into training and testing sets. This method ensures that the model is generalizable and not overfitted to a particular subset of data. In the context of nanotechnology, cross validation is crucial for the development of reliable and robust models, especially when dealing with small-scale phenomena and complex materials.
