cross validation in crf++

Question

I was wondering how to do cross validation in CRF++. It was written in the documentation:

crf_learn -f 3 -c 1.5 template_file train_file model_file

-c float:

With this option, you can change the hyper-parameter for the CRFs. With larger C value,
CRF tends to overfit to the give training corpus. This parameter trades the balance
between overfitting and underfitting. The results will significantly be influenced 
by this parameter. You can find an optimal value by using held-out data or more 
general model selection method such as cross validation.

How can one do a cross validation as mentioned in this manual

Answer 1

If my understanding is correct, CRF++ doesn't have cross validation functionality in-built. we have to do it seperately

Answer 2

The manual is trying to say that you can figure out the optimal value of C parameter by performing cross validation or testing on a held-out set yourself. CRF++ doesn't have such a functionality. Thanks