Accelerating the code for random selection of polygons

You can use spatial index by sindex method in geopandas. I've tested on three datasets include 100, 1000, 10000 points (instead of polygons), respectively. I've used different number of tiles.

# without spatial index (for loop in the question)
outputs = []          
for tile in tiles:                
    poly = Polygon(tile)
    ok = gdf[gdf.geometry.intersects(poly)]
    if ok.shape[0] >= 1:
        out = ok.sample(1)
        outputs.append(out)

---

# with spatial index
sindex = gdf.sindex
outputs = []          
for tile in tiles:
    poly = Polygon(tile)
    candidates_index = list(sindex.intersection(poly.bounds))
    candidates = gdf.iloc[candidates_index]
    matches = candidates[candidates.intersects(poly)]
    if matches.shape[0] >= 1:
        out = matches.sample(1)
        outputs.append(out)

RESULTS: (times for for loop in seconds)

   Number Of        No Index   Index
Tiles   Points      (sec)     (sec)
--------------------------------------------
        100         0.10       0.10
40      1000        0.50       0.12
        10000       3.50       0.23
--------------------------------------------
        100         1.4        1.6
560     1000        5.6        1.6
        10000       50         1.6
--------------------------------------------
        100         3.5        4.5
1420    1000        15         4.5
        10000       132        4.0
--------------------------------------------
        100         8          10
3096    1000        34         10
        10000       392        10

As you can see, increase in number of points increases times extremely when not using index, but no changing when using index. When using index, in that case, number of tiles is important.

EDIT: If you have memory problem with tiles list, then you can use generator.

# Just change outer [] into (). tiles is not a list anymore, but a generator.
# convert tiles = [ ... ] to tiles = ( ... )
tiles = ([(ulx, uly), (ulx, lry), (lrx, lry), (lrx, uly)] for ulx, uly, lrx, lry in zip(ulx_s, uly_s, lrx_s, lry_s))
# remove print line. because a generator has no len function
print(len(tiles))

If there are (far) more polygons than grid cells, you should invert your computation, making the outer loop over the polygons. Something like:

for poly in  polygons:
  bb = boundingBox(poly)
  compute list of grid cells intersecting/containing the bb. #Note this is NOT a polygon    
      #intersection, it's a simple comparison of bounds
  for each overlapping grid cell, add poly to the list of overlapping boxes

for each cell in grid_cells:
  sample one overlapping box from list
  test to see if the polygon actually intersects the grid cell
  if false, delete the box from the list and sample again
  else add poly to your output

I also note that you are say you want 1km grid cells, but you're working in lat/lon coordinates and using a conversion of 0.008983157 degrees = 1km. That's correct for longitudes at the equator, but gets increasingly bad as you move away from the equator. You really should work in a projected coordinate system, like UTM, where the coordinates are in distance units.