Categorical variables with only two values
There are only a few cases where LabelEncoder is useful because of the ordinality issue. If your categorial features are ordinal then use LabelEncoder otherwise use One-hot encoding. But, One-hot encoding increases dimension. In this case, I typically employ One-hot encoding followed by PCA for dimensionality reduction.
I am dealing with different datasets that have only Categorical variables/features with only two values such as (temperature = 'low' and 'high') or (light = 'on' and 'off' or '0' and '1'). 
I am not really sure whether to use "one-hot encoding" or "Label Encoding" method to train my models. 
I am working on a classification problem and using some supervised machine learning algorithms. 
I used "Label Encoding" and I got a pretty good result. I feel there could be something that I did was wrong. I am not sure if I should use "one-hot encoding" or not.
In case of Categorical variables with only two values which method should I use to convert the variables?