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 
 A: The main goals behind K-fold cross validation are 


*

*Selecting one model among many with an objective criterai that relates to the model's usefulness.

*Have a first idea on how the model will perform.


So, what I would do is to take the model than perforemd best in k-fold cross validation (or maybe, the simplest model that did well enough). Then you can train this model with your entire training dataset and start the testing phase (to further check for overfitting or other issues)
A: Although it seems very late to put a comment for an answer to your question, I do not really agree with the answer provided above. If you run a random forest prediction model with a 10-fold-cross-validation, you get only one model and get the model performance with average statistics(usually). If you have two random forests with different model parameters, then you get two models and you run 10-fold-cross-validation two times.
Then you compare the model performances against each other. You can pick the best one out of the two models and train the model with the whole data and then run on the test data to get the final model performance. This is how I understand.
