R squared comparison I have 5 features in my data.
The R squared value when I use features 1,2, and 3 is $x$ and the R squared value when I use features 1,3, and 4 is $x + 0.1.$
Does this mean my second model is better than first model?

The answer comes down to what you might mean by "better." $R^2$ is an appropriate measure of goodness in an Ordinary Least Squares regression, provided you are confident all the conditions needed for its application apply.
Here is a simple example to illustrate the point.
A response variable $y$ is plotted on the vertical axes against two (uncorrelated) explanatory variables (both of which exhibit the same range from $1$ through $8$).  The univariate least-squares fits and $R^2$ values are shown.  You decide which is the better model.  Is the issue really settled by a mere comparison of the $R^2$ values?

