Does cross validation reduce underfitting? I know that cross validation helps reduce overfitting in the data by its property of testing and training on all the instances. But is there any chance that it also reduces underfitting? I mean, if we have fewer instances in data and we don't want our model to be more general (underfit). We can use cross validation to reduce the effect of underfitting as well. But I am not sure about it. Please let me know if that's correct what I am thinking and if cross-fold-validation reduces underfitting as well.

The short answer is No. Cross validation does not "reduce the effects of underfitting" — or overfitting, for that matter.
I agree with the comments that your question seems to miss the point a little.
The purpose of validation is to evaluate model performance after fitting, not to make the model more or less fit. It's the model's parameters that control its fitness, and tools like regularization can help you control its complexity. Validation (CV or holdout) might help you detect under- or over-fitness, but it doesn't improve or avoid either one.
I strongly recommend reading Raschka 2018, it's really the best thing that's been written about validation and it might help you figure out the best strategy for your task.
