In simple words: Gradient descent updates weights; learning rate controls the step size.
Follow the slope
Gradient descent is a way to reduce loss by changing weights in the direction that tends to lower it. The gradient tells the direction and sensitivity of each weight.
Choose the step size
The learning rate sets how big each update is. A very large rate can overshoot a good solution. A very small one can make learning slow. Training usually needs many updates, not one giant correction.
See the flow
loss→gradient→weight update→new loss
Simple example
Imagine walking downhill in fog. You feel the slope, take a measured step, and check again.
Remember: Gradient descent updates weights; learning rate controls the step size.