Model selection and assessment using leave-one-out cross validation
What you just said is dividing the data into three parts: training, validating and testing. 
This is a very common practice in machine learning.
We use validation to help in selecting hyper parameters. 
Going with this option vs. Leave one out vs. cross validation depends mainly on how many number of samples you have. If you have a lot of samples, then going with the option of splitting the data into three parts can be more efficient.
I am not sure about your statement of unbiased. You need to be careful when you make judgment