# What is after doing k-fold cross-validation?

When we do K-fold cross validation, we are testing how well our model is able to get trained by some data and then predict data it hasn't seen.

I selected 9 fold for training, and 1 fold for validation. Training set would be 8 images, and validation set would be 2 images. I have Trained my model with training set, and computed performance with validation set. I have 10 training sets, 10 validation sets, 10 models, and 10 errors.

now What can I do

Do I need to choose a predictive model after I did k-fold cross-validation?

I have read a lot. But I do not understand what is the next step

• cross validation tells you what is the mean error and its variance when using your chosen model and hyperparameters. It is considered to be a good estimate of how this model performs on unseen data. – Jan Kukacka Jul 8 at 10:07

• This answer is very ambiguous (in particular with the difficulties in understanding expressed in the question): you do not select one of the $k$ so-called surrogate models calculated during the cross validation. This answer refers to cross validating $n$ different models with e.g. $n$ different sets of hyperparameters. So inside all the cross validations, $n \times k$ surrogate models are trained. The $k$ surrogate models with the same hyperparameter set are assumed to be equal (or equivalent), and no selection takes place among them! – cbeleites supports Monica Jul 10 at 10:59